



'''simple docstring'''



import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_  :							List[Any]             =    logging.get_logger()
def        UpperCAmelCase (    A	:					int       ,		A	:					str       ,		A	:					LevitConfig       ,		A	:					Path       ,		A	:					bool = True					):
							print(F"""Converting {name}..."""					)

							with torch.no_grad():
														if hidden_sizes == 128:
																					if name[-1] == "S":
																												SCREAMING_SNAKE_CASE       :   Tuple         =      timm.create_model('''levit_128s'''       ,		pretrained=A					)
																					else:
																												SCREAMING_SNAKE_CASE       :   str         =      timm.create_model('''levit_128'''       ,		pretrained=A					)
														if hidden_sizes == 192:
																					SCREAMING_SNAKE_CASE       :   Tuple         =      timm.create_model('''levit_192'''       ,		pretrained=A					)
														if hidden_sizes == 256:
																					SCREAMING_SNAKE_CASE       :   List[str]         =      timm.create_model('''levit_256'''       ,		pretrained=A					)
														if hidden_sizes == 384:
																					SCREAMING_SNAKE_CASE       :   Tuple         =      timm.create_model('''levit_384'''       ,		pretrained=A					)

														from_model.eval()
														SCREAMING_SNAKE_CASE       :   int         =      LevitForImageClassificationWithTeacher(A					).eval()
														SCREAMING_SNAKE_CASE       :   Tuple         =      OrderedDict()

														SCREAMING_SNAKE_CASE       :   int         =      from_model.state_dict()
														SCREAMING_SNAKE_CASE       :   int         =      list(from_model.state_dict().keys()					)
														SCREAMING_SNAKE_CASE       :   Optional[Any]         =      list(our_model.state_dict().keys()					)
														print(len(A					)       ,		len(A					)					)
														for i in range(len(A					)					):
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      weights[og_keys[i]]
														our_model.load_state_dict(A					)

														SCREAMING_SNAKE_CASE       :   Optional[int]         =      torch.randn((2, 3, 224, 224)					)
														SCREAMING_SNAKE_CASE       :   List[Any]         =      from_model(A					)
														SCREAMING_SNAKE_CASE       :   List[Any]         =      our_model(A					).logits

							assert torch.allclose(A       ,		A					), "The model logits don't match the original one."

							SCREAMING_SNAKE_CASE       :   Any         =      name
							print(A					)

							if push_to_hub:
														our_model.save_pretrained(save_directory / checkpoint_name					)
														SCREAMING_SNAKE_CASE       :   Any         =      LevitImageProcessor()
														image_processor.save_pretrained(save_directory / checkpoint_name					)

														print(F"""Pushed {checkpoint_name}"""					)
def        UpperCAmelCase (    A	:					Path       ,		A	:					str = None       ,		A	:					bool = True					):
							SCREAMING_SNAKE_CASE       :   Tuple         =      '''imagenet-1k-id2label.json'''
							SCREAMING_SNAKE_CASE       :   Tuple         =      1000
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      (1, num_labels)

							SCREAMING_SNAKE_CASE       :   Tuple         =      '''huggingface/label-files'''
							SCREAMING_SNAKE_CASE       :   Dict         =      num_labels
							SCREAMING_SNAKE_CASE       :   List[str]         =      json.load(open(hf_hub_download(A       ,		A       ,		repo_type='''dataset'''					)       ,		'''r'''					)					)
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      {int(A					): v for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE       :   str         =      idalabel
							SCREAMING_SNAKE_CASE       :   Dict         =      {v: k for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      partial(A       ,		num_labels=A       ,		idalabel=A       ,		labelaid=A					)

							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      {
							    '''levit-128S''': 128,
							    '''levit-128''': 128,
							    '''levit-192''': 192,
							    '''levit-256''': 256,
							    '''levit-384''': 384,
							}

							SCREAMING_SNAKE_CASE       :   Dict         =      {
							    '''levit-128S''': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]       ,		num_attention_heads=[4, 6, 8]       ,		depths=[2, 3, 4]       ,		key_dim=[16, 16, 16]       ,		drop_path_rate=0       ,		),
							    '''levit-128''': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]       ,		num_attention_heads=[4, 8, 12]       ,		depths=[4, 4, 4]       ,		key_dim=[16, 16, 16]       ,		drop_path_rate=0       ,		),
							    '''levit-192''': ImageNetPreTrainedConfig(
							        hidden_sizes=[192, 288, 384]       ,		num_attention_heads=[3, 5, 6]       ,		depths=[4, 4, 4]       ,		key_dim=[32, 32, 32]       ,		drop_path_rate=0       ,		),
							    '''levit-256''': ImageNetPreTrainedConfig(
							        hidden_sizes=[256, 384, 512]       ,		num_attention_heads=[4, 6, 8]       ,		depths=[4, 4, 4]       ,		key_dim=[32, 32, 32]       ,		drop_path_rate=0       ,		),
							    '''levit-384''': ImageNetPreTrainedConfig(
							        hidden_sizes=[384, 512, 768]       ,		num_attention_heads=[6, 9, 12]       ,		depths=[4, 4, 4]       ,		key_dim=[32, 32, 32]       ,		drop_path_rate=0.1       ,		),
							}

							if model_name:
														convert_weight_and_push(
														    names_to_hidden_sizes[model_name]       ,		A       ,		names_to_config[model_name]       ,		A       ,		A					)
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(names_to_hidden_sizes[model_name]       ,		A       ,		A       ,		A       ,		A					)
							return config, expected_shape


if __name__ == "__main__":
							lowerCAmelCase_  :							Dict             =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default=None,
							    type=str,
							    help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default='levit-dump-folder/',
							    type=Path,
							    required=False,
							    help='Path to the output PyTorch model directory.',
							)
							parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
							parser.add_argument(
							    '--no-push_to_hub',
							    dest='push_to_hub',
							    action='store_false',
							    help='Do not push model and image processor to the hub',
							)

							lowerCAmelCase_  :							List[str]             =    parser.parse_args()
							lowerCAmelCase_  :							Path             =    args.pytorch_dump_folder_path
							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
							convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)









'''simple docstring'''



import os

lowerCAmelCase_  :							Union[str, Any]             =    {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}
def        UpperCAmelCase (    A	:					str					):
							SCREAMING_SNAKE_CASE       :   str         =      0

							SCREAMING_SNAKE_CASE       :   List[str]         =      0
							while index < len(A					) - 1:
														SCREAMING_SNAKE_CASE       :   Dict         =      SYMBOLS[numerals[index]]
														SCREAMING_SNAKE_CASE       :   str         =      SYMBOLS[numerals[index + 1]]
														if current_value < next_value:
																					total_value -= current_value
														else:
																					total_value += current_value
														index += 1
							total_value += SYMBOLS[numerals[index]]

							return total_value
def        UpperCAmelCase (    A	:					int					):
							SCREAMING_SNAKE_CASE       :   Any         =      ''''''

							SCREAMING_SNAKE_CASE       :   Dict         =      num // 1000
							numerals += m_count * "M"
							num %= 1000

							SCREAMING_SNAKE_CASE       :   str         =      num // 100
							if c_count == 9:
														numerals += "CM"
														c_count -= 9
							elif c_count == 4:
														numerals += "CD"
														c_count -= 4
							if c_count >= 5:
														numerals += "D"
														c_count -= 5
							numerals += c_count * "C"
							num %= 100

							SCREAMING_SNAKE_CASE       :   Any         =      num // 10
							if x_count == 9:
														numerals += "XC"
														x_count -= 9
							elif x_count == 4:
														numerals += "XL"
														x_count -= 4
							if x_count >= 5:
														numerals += "L"
														x_count -= 5
							numerals += x_count * "X"
							num %= 10

							if num == 9:
														numerals += "IX"
														num -= 9
							elif num == 4:
														numerals += "IV"
														num -= 4
							if num >= 5:
														numerals += "V"
														num -= 5
							numerals += num * "I"

							return numerals
def        UpperCAmelCase (    A	:					str = "/p089_roman.txt"					):
							SCREAMING_SNAKE_CASE       :   List[str]         =      0

							with open(os.path.dirname(A					) + roman_numerals_filename					) as filea:
														SCREAMING_SNAKE_CASE       :   str         =      filea.readlines()

							for line in lines:
														SCREAMING_SNAKE_CASE       :   Any         =      line.strip()
														SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      parse_roman_numerals(A					)
														SCREAMING_SNAKE_CASE       :   Any         =      generate_roman_numerals(A					)
														savings += len(A					) - len(A					)

							return savings


if __name__ == "__main__":
							print(f'{solution() = }')




